import tensorflow as tf

def inference(images, batch_size, n_classess):
    #conv1,shape=[kernel size, kernel size, channels, kernel numbers]
    #第一个卷积层
    with tf.variable_scope('conv1') as scope:
        weights = tf.get_variable('weights', shape = [3, 3, 3, 16], dtype = tf.float32,
                                  initializer = tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases', shape=[16], dtype = tf.float32, 
                                 initializer = tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(images, weights, strides=[1,1,1,1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name= scope.name)
        
    #pool1 and norm1
    #池化层
    with tf.variable_scope('pooling1_lrn') as scope:
        pool1 = tf.nn.max_pool(conv1, ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME',name='poolong1')
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1, alpha=0.001/9.0, beta=0.75, name='norm1')
        
    #conv2第二卷积层
    with tf.variable_scope('conv2') as scope:
        weights = tf.get_variable('weights', shape=[3,3,16,16], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[16], 
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(norm1, weights, strides=[1,1,1,1],padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name='conv2')
    
    #pool2 and norm2
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4,bias=1,alpha=0.001/9,beta=0.75,name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1,3,3,1],strides=[1,1,1,1],padding='SAME',name='pooling2')
    
    
    #local3
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.get_variable('weights', shape=[dim,128],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
        biases = tf.get_variable('biases',shape=[128], dtype=tf.float32,initializer=tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
    
    #local4
    with tf.variable_scope('local4') as scope:
        weights = tf.get_variable('weights',shape=[128,128],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
        biases = tf.get_variable('biases', shape=[128],dtype=tf.float32, initializer=tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3,weights) + biases, name = 'local4')
    #softmax
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.get_variable('softmax_linear',shape=[128, n_classess],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
        biases = tf.get_variable('biases',shape=[n_classess],dtype=tf.float32,initializer=tf.constant_initializer(0.1))
        softmax_linear = tf.add(tf.matmul(local4, weights),biases,name='softmax_linear')
        
    return softmax_linear

def losses(logits, labels):
    with tf.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels,name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.summary.scalar(scope.name+'/loss',loss)
    return loss

def trainning(loss, learning_rate):
    with tf.name_scope('optimizer'):
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        global_step = tf.Variable(0, name='global_step',trainable=False)
        train_op = optimizer.minimize(loss, global_step=global_step)
    return train_op


def evaluation(logits, labels):
    with tf.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits,labels,1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.summary.scalar(scope.name+'/accuracy',accuracy)
    return accuracy


    
    
    
    
    
    
    
    
    
    
